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20122026
most citedEnhanced Compressed Sensing Recovery with Level Set Normals

16 citations · 39 across the 10 of their papers we have counts for

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9 papers · 1 filter

cs.CV2024

GASP: Gaussian Avatars with Synthetic Priors

Jack Saunders, Charlie Hewitt, Yanan Jian +8

Gaussian Splatting has changed the game for real-time photo-realistic rendering. One of the most popular applications of Gaussian Splatting is to create animatable avatars, known a…

cs.CV2023★ 1 cited

BlendFields: Few-Shot Example-Driven Facial Modeling

Kacper Kania, Stephan J. Garbin, Andrea Tagliasacchi +5

Generating faithful visualizations of human faces requires capturing both coarse and fine-level details of the face geometry and appearance. Existing methods are either data-driven…

cs.CV2022★ 1 cited

Photo-realistic 360 Head Avatars in the Wild

Stanislaw Szymanowicz, Virginia Estellers, Tadas Baltrusaitis +1

Delivering immersive, 3D experiences for human communication requires a method to obtain 360 degree photo-realistic avatars of humans. To make these experiences accessible to all,…

cs.CV2021★ 1 cited

Fake It Till You Make It: Face analysis in the wild using synthetic data alone

Erroll Wood, Tadas Baltrušaitis, Charlie Hewitt +5

We demonstrate that it is possible to perform face-related computer vision in the wild using synthetic data alone. The community has long enjoyed the benefits of synthesizing train…

cs.CV2020★ 4 cited

A high fidelity synthetic face framework for computer vision

Tadas Baltrusaitis, Erroll Wood, Virginia Estellers +6

Analysis of faces is one of the core applications of computer vision, with tasks ranging from landmark alignment, head pose estimation, expression recognition, and face recognition…

cs.CV2020

CONFIG: Controllable Neural Face Image Generation

Marek Kowalski, Stephan J. Garbin, Virginia Estellers +3

Our ability to sample realistic natural images, particularly faces, has advanced by leaps and bounds in recent years, yet our ability to exert fine-tuned control over the generativ…

cs.CV2019★ 1 cited

Contrastive Learning for Lifted Networks

Christopher Zach, Virginia Estellers

In this work we address supervised learning of neural networks via lifted network formulations. Lifted networks are interesting because they allow training on massively parallel ha…

cs.CV2017

Compression for Smooth Shape Analysis

V. Estellers, F. R. Schmidt, D. Cremers

Most 3D shape analysis methods use triangular meshes to discretize both the shape and functions on it as piecewise linear functions. With this representation, shape analysis requir…

cs.CV2012★ 16 cited

Enhanced Compressed Sensing Recovery with Level Set Normals

Virginia Estellers, Jean-Philippe Thiran, Xavier Bresson

We propose a compressive sensing algorithm that exploits geometric properties of images to recover images of high quality from few measurements. The image reconstruction is done by…